Real-time PCR detection of<i>Neisseria gonorrhoeae</i>susceptibility to penicillin
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to develop a real-time PCR assay targeting the gonococcal porB gene (PorB-PCR) for predicting susceptibility of Neisseria gonorrhoeae to penicillin. This complements a previously described PCR assay for detecting penicillinase-producing N. gonorrhoeae (PPNG) developed by our laboratory (PPNG-PCR). METHODS: The PorB-PCR assay was designed using six probes to characterize various combinations of amino acids at positions 101 and 102 of the PorB1b class protein, including the WT G101/A102 and mutant G101K/A102D, G101K/A102N and G101K/A102G sequences, as well as the PorB1a sequence. The ability of these sequences to predict penicillin susceptibility was initially assessed using 2307 N. gonorrhoeae isolates from throughout Australia for which phenotypic susceptibility data were available. The assay was then applied to N. gonorrhoeae-positive clinical specimens (n = 70). Specificity was assessed by testing commensal Neisseria strains (n = 75) and N. gonorrhoeae-negative clinical specimens (n = 171). RESULTS: Testing of the 2307 N. gonorrhoeae isolates using PorB-PCR to detect G101/A102 and PorB1a sequences identified a total of 78.4% (61.2% and 17.2%, respectively) of penicillin-susceptible isolates with specificities of 97.4% and 99.3% and positive predictive values of 98.8% and 98.9%, where PPNG strains were simultaneously identified and excluded. Similar performance data were obtained when the PorB-PCR assay was applied to the N. gonorrhoeae-positive clinical specimens. No false-positive results were observed for the N. gonorrhoeae-negative samples and no cross-reactions were observed with the non-gonococcal species. CONCLUSIONS: When used in parallel with the previously described PPNG-PCR, the PorB-PCR approach has the potential to facilitate individualized treatment of gonorrhoea using penicillin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".